Image recognition processing device, method, and program for processing of image information obtained by imaging the surrounding area of a vehicle

ABSTRACT

A feature information collecting device includes: a feature image recognizing unit that performs image recognition processing of a feature included in image information in a surrounding area of a vehicle; a construction information obtaining unit that obtains construction information including information of a construction section; a construction information storage unit that stores the obtained construction information; a construction completion determining unit that determines, when the vehicle travels a section of a road corresponding to a construction section according to the already stored construction information, completion of construction indicated by the construction information; and a feature learning unit that causes the feature image recognizing unit to perform image recognition processing of a feature in a construction section according to the construction information when the completion of construction is determined, and that generates, based on an image recognition result thereof and the vehicle position information, learned feature information including position information and attribute information of an image-recognized feature.

INCORPORATION BY REFERENCE

The disclosure of Japanese Patent Application No. 2007-337454 filed onDec. 27, 2007 including the specification, drawings and abstract isincorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a feature information collecting deviceand a feature information collecting program that recognizes the imageof a feature included in image information obtained from an imagingdevice or the like mounted in a vehicle and collects information of thefeature, as well as a vehicle position recognizing device and anavigation device using them.

2. Description of the Related Art

Accompanying improvement in imaging devices and image recognitiontechnologies in recent years, it has become more common to develop atechnology for performing image recognition processing of imageinformation obtained by imaging the surrounding area of a vehicle by anon-vehicle camera, and correcting vehicle position informationrepresenting the current positing of the vehicle based on surroundingconditions of the vehicle indicated in the image recognition result. Forexample, the vehicle position recognizing device described in JapanesePatent Application Publication No. JP-A-2007-271568 is structured toperform image recognition processing of a target feature included inimage information obtained by an imaging device mounted in a vehicle,and check the image recognition result with feature information of thistarget feature stored in advance in a database to thereby correct thevehicle position information obtained by a GPS signal or autonomousnavigation. Thus, highly precise vehicle position information can beobtained.

Incidentally, for correcting vehicle position information by such avehicle position recognizing device, it is necessary to prepare a highlyaccurate database of feature information. To collect such featureinformation, there is a known device which collects feature informationbased on an image recognition result of a feature included in imageinformation of the surrounding area of a vehicle obtained from animaging device or the like mounted in the vehicle (for example, refer toJapanese Patent Application Publication No. JP-A-2006-038558 andJapanese Patent Application Publication No. JP-A-2006-275690).Incidentally, all of the devices described in Japanese PatentApplication Publication No. JP-A-2007-271568, Japanese PatentApplication Publication No. JP-A-2006-038558 and Japanese PatentApplication Publication No. JP-A-2006-275690 take a traffic sign or aroad traffic information display board provided on a road as a targetfeature. Then, these devices perform image recognition of a targetfeature included in image information obtained by an imaging devicemounted in the vehicle, and associate feature information such as signinformation extracted from the recognition result thereof with positioninformation and/or section information and store them in a map database.At this time, the position information and/or section informationassociated with the feature information is determined based oninformation from a GPS receiver, a gyro, a vehicle speed sensor and thelike used generally in navigation devices. Thus, a database of featureinformation is created in these devices, and a route search, drivingsupport, and the like based on the database is possible.

SUMMARY OF THE INVENTION

In the vehicle position recognizing device described in Japanese PatentApplication Publication No. JP-A-2007-271568, with reference to positioninformation of a feature included in feature information which isprepared in advance and stored in a database, vehicle positioninformation can be corrected using a positional relationship between thevehicle and this feature based on an image recognition result of thefeature. Thus, the device has an advantage that the vehicle position canbe identified with very high accuracy. However, since the featureinformation stored in the database is used as the reference in thevehicle position recognizing device, when the position of an actualfeature is moved to a position different from a position indicated bythe feature information due to road construction or the like, there is apossibility that an error of the vehicle position will increase due tocorrection of the vehicle position information.

Thus, when road construction or the like takes place, it becomesnecessary to modify the feature information stored in the database.However, the interval of updating the database in a navigation device isnormally every year, and thus it is difficult to respond quickly tomodification of feature information accompanied by road construction orthe like. Accordingly, it is conceivable to collect feature informationand reflect it in the database based on an image recognition result of afeature included in image information of the surrounding area of thevehicle obtained from an imaging device or the like mounted in thevehicle. However, no technology has been existed to collect featureinformation for the purpose of modifying feature information in the casewhere the position of the feature is moved due to road construction orthe like, the type of the feature is changed, or the like.

The present invention is made in view of the above-described problems,and it is an object thereof to provide a feature information collectingdevice and a feature information collecting program capable of quicklycollecting, even in a case where the position of a feature is moved, orthe type of a feature is changed due to construction, featureinformation after the construction, as well as a vehicle positionrecognizing device and a navigation device using them.

To achieve the above object, a feature information collecting deviceaccording to the present invention has a characteristic structure thatincludes: a vehicle position information obtaining unit that obtainsvehicle position information representing a current position of avehicle; an image information obtaining unit that obtains imageinformation in a surrounding area of the vehicle; a feature imagerecognizing unit that performs image recognition processing of a featureincluded in the image information; a construction information obtainingunit that obtains construction information including information of aconstruction section; a construction information storage unit thatstores the construction information obtained by the constructioninformation obtaining unit; a construction completion determining unitthat determines, when the vehicle travels a section of a roadcorresponding to a construction section according to the constructioninformation already stored in the construction information storage unit,completion of construction indicated by the construction information;and a feature learning unit that causes the feature image recognizingunit to perform image recognition processing of a feature in aconstruction section according to the construction information when thecompletion of construction is determined by the construction completiondetermining unit, and that generates, based on an image recognitionresult thereof and the vehicle position information, learned featureinformation including position information and attribute information ofan image-recognized feature.

With this characteristic structure, obtained construction information isstored in the construction information storage unit, and when a vehicletravels a section of a road corresponding to a construction sectionaccording to the stored existing construction information, completion ofconstruction is determined. When the construction is completed, theposition and the attribute of a feature are learned based on an imagerecognition result of the feature and vehicle position information togenerate learned feature information. Accordingly, a section in whichconstruction is performed in the past can be recognized appropriately,and learning of a feature can be performed targeting this section.Therefore, even in a case where the position of a feature is moved, orthe type of a feature is change due to construction, feature informationafter the construction can be collected quickly.

Here, in another preferable structure, the construction informationobtaining unit includes a construction information receiving unit thatreceives the construction information from a transmitter disposedoutside of the vehicle.

With this structure, when construction information is transmitted from atransmitter disposed outside of the vehicle, this information can bereceived and construction information can be obtained appropriately.

Further, in another preferable structure, the construction informationobtaining unit includes a construction image recognizing unit thatperforms image recognition processing of a construction symbol includedin the image information obtained by the image information obtainingunit, and a construction information generating unit that generates theconstruction information based on an image recognition result of aconstruction symbol by the construction image recognizing unit.

With this structure, construction information can be obtainedappropriately based on image information of the surrounding area of thevehicle obtained by the image information obtaining unit.

Here, in another preferable structure, the construction imagerecognizing unit performs image recognition processing of at least oneof a construction notice sign, a construction fence, a constructionbarricade, a security light, a cone, and a construction guide humanmodel as the construction symbol.

With this structure, a construction symbol disposed with highprobability at a site where construction is carried out can be taken asa target for image recognition processing, and thus the possibility ofappropriately obtaining construction information can be increased.

Further, in another preferable structure, the construction informationgenerating unit sets a predetermined section with reference to arecognition position of the construction symbol as information of theconstruction section included in the construction information.

With this structure, information of a construction section can beobtained appropriately regardless of whether or not a constructionsection can be identified from an image recognition result of aconstruction symbol.

Further, in another preferable structure, when construction symbols areincluded in image information of a plurality of consecutive frames, theconstruction information generating unit sets a start point of theconstruction section with reference to a recognition position of a firstconstruction symbol included in image information of a front side of thevehicle, and sets an end point of the construction section withreference to a recognition position of a last construction symbolincluded in image information of a rear side of the vehicle.

With this structure, when a construction symbol is included in imageinformation of a plurality of consecutive frames, it is possible toappropriately set a start point and an end point of a constructionsection based on an image recognition result for image information of afront side and a rear side of the vehicle, and obtain information of theconstruction.

Further, in another preferable structure, when a construction noticesign is image-recognized as the construction symbol by the constructionimage recognizing unit and a construction section is recognized based onan image recognition result of the construction notice sign, theconstruction information generating unit sets information of aconstruction section included in the construction information accordingto a recognition result of the construction section.

With this structure, when the construction symbol is a constructionnotice sign, and a construction section can be identified based on animage recognition result thereof, it is possible to obtain informationof the construction section appropriately.

Further, in another preferable structure, when the vehicle travels asection of a road corresponding to a construction section according tothe construction information already stored in the constructioninformation storage unit, the construction completion determining unitdetermines that construction indicated by the construction informationis completed when construction information including a same constructionsection is not obtained by the construction information obtaining unit.

With this structure, using an obtaining state of constructioninformation by the construction information obtaining unit, it becomespossible to appropriately determine completion of construction accordingto existing construction information stored in the constructioninformation storage unit.

Further, in another preferable structure, when the constructioninformation includes information of a construction period, theconstruction completion determining unit determines that constructionindicated by the construction information is completed if theconstruction period according to the construction information is overwhen the vehicle travels a section of a road corresponding to aconstruction section according to the construction information alreadystored in the construction information storage unit.

With this structure, based on information of a construction periodincluded in the construction information, it becomes possible toappropriately determine completion of construction according to existingconstruction information stored in the construction information storageunit.

Further, in another preferable structure, when the vehicle does nottravel a section of a road corresponding to a construction sectionaccording to the construction information already stored in theconstruction information storage unit for a predetermined time period,the construction information is deleted from the constructioninformation storage unit.

With this structure, it is possible to prevent that the constructioninformation stored in the construction information storage unit remainswithout being deleted while completion determination is not performed bythe construction completion determining unit. Thus, it is possible tosuppress the amount of construction information stored in theconstruction information storage unit from becoming excessively large.

Further, in another preferable structure, the feature learning unitincludes: a recognition result storage unit that stores recognitionposition information, which represents a recognition position of afeature by the feature image recognition unit and is obtained based onthe vehicle position information, and attribute information of thefeature in an associated manner; an estimated position determining unitthat determines, based on a plurality of the recognition positioninformation for a same feature, which are stored in the recognitionresult storage unit due to the same feature being image-recognized aplurality of times, an estimated position of the feature; and a learnedfeature information generating unit that generates learned featureinformation by associating position information representing anestimated position of each feature determined by the estimated positiondetermining unit with attribute information of the feature.

With this structure, based on a plurality of recognition positioninformation for the same feature, which are stored in the recognitionresult storage unit due to the same feature being image-recognized aplurality of times, an estimated position of this feature is determined,and learned feature information having the estimated position asposition information is generated. Therefore, even when errors areincluded in recognition positions of a target feature indicated byrespective recognition position information, the errors can be averagedby determining the estimated position using a plurality of recognitionposition information, and hence accuracy of position information of afeature included in the learned feature information can be increased.

Further, another preferable structure further includes a featuredatabase that stores initial feature information including positioninformation and attribute information which are prepared in advance fora plurality of features, in which the feature learning unit causes thefeature image recognizing unit to perform image recognition in theconstruction section giving priority to a feature of a same type as afeature according to the initial feature information having positioninformation in the construction section.

In general, even when road construction is performed, there is a lowpossibility that a feature completely different from the feature existedbefore the construction is provided after the construction. Supposingthat the position of the feature is changed, there is a high possibilitythat a feature of the same type as the feature existed before theconstruction exists after the construction. With this structure, whencausing the feature image recognizing unit to perform image recognitionprocessing of a feature in a construction section according to theconstruction information already stored in the construction informationstorage unit, a feature of the same type as the feature existed beforethe construction can be image-recognized with priority, because there isa high possibility that this feature exists after the construction.Thus, a possibility of succeeding in image recognition of a feature canbe increased.

Further, in another preferable structure, the feature learning unitcompares an image recognition result of a feature by the feature imagerecognizing unit with the initial feature information having positioninformation in the construction section and changes a generatingcondition for the learned feature information according to a degree ofapproximation therebetween.

With this structure, the generation condition of learned featureinformation is changed according to the degree of approximation of theposition, the type, or the like of a feature before and afterconstruction. Therefore, learning is performed easily when a changeamount of the position or the type of a feature before and afterconstruction is small, for example, when the feature is not moved orchanged before and after construction, or only the position is moved.Thus, it becomes possible to generate learned feature informationquickly.

Further, another preferable structure includes a database that storesthe learned feature information.

With this structure, when correction of vehicle position information isperformed, the generated learned feature information can be used easily.

A vehicle position recognition device according to the present inventionhas a characteristic structure that includes: a feature informationcollecting device having the above structures; and a vehicle positioncorrecting unit that checks an image recognition result of a feature bythe feature image recognizing unit with the learned feature informationfor the feature and corrects the vehicle position information.

With this characteristic structure, by checking an image recognitionresult of a feature by the feature image recognizing unit with thelearned feature information for this feature to correct vehicle positioninformation, it is possible to correct the vehicle position informationwith reference to position information of a feature included in thelearned feature information, and identify the vehicle position with highaccuracy. Therefore, even in the case where the position of a feature ismoved, or the type of the feature is changed due to construction, thevehicle position information can be corrected using the learned featureinformation collected after the construction.

The above-described vehicle position recognizing device according to thepresent invention can be used preferably for a navigation deviceincluding a map database in which map information is stored, anapplication program that operates referring to the map information, anda guidance information output unit that operates according to theapplication program and outputs guidance information.

A technical characteristic of a feature information collecting deviceaccording to the present invention having the above structures isapplicable to a feature information collecting method and a featureinformation collecting program, and thus the present invention caninclude such a method and program as subjects of right. For example, thefeature information collecting program causes a computer to execute: avehicle position information obtaining function that obtains vehicleposition information representing a current position of a vehicle; animage information obtaining function that obtains image information in asurrounding area of the vehicle; a feature image recognizing functionthat performs image recognition processing of a feature included in theimage information; a construction information obtaining function thatobtains construction information including information of a constructionsection; a construction information storage function that stores theconstruction information obtained by the construction informationobtaining unit in a construction information storage unit; aconstruction completion determining function that determines, when thevehicle travels a section of a road corresponding to a constructionsection according to the construction information already stored in theconstruction information storage unit, completion of constructionindicated by the construction information; and a feature learningfunction that performs image recognition processing of a feature in aconstruction section according to the construction information when thecompletion of construction is determined by the construction completiondetermining unit, and that generates, based on an image recognitionresult thereof and the vehicle position information, learned featureinformation including position information and attribute information ofan image-recognized feature. As a matter of course, such featureinformation collecting program can obtain the above-described operationand effect related to the feature information collecting device, and canfurther incorporate the several additional technologies presented asexamples of preferable structures thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a schematic structure of a navigationdevice according to an embodiment of the present invention;

FIG. 2 is a diagram showing a vehicle in which the navigation device ismounted;

FIG. 3 is a diagram showing an example of a structure of map informationstored in a map database;

FIG. 4 is a diagram showing an example of feature information of a roadmarking stored in the feature database;

FIG. 5 is a diagram showing an example of a condition of the vehiclewhen image recognizing a target feature and correcting vehicle positioninformation;

FIGS. 6A and 6B are explanatory diagrams illustrating examples of anobtaining method of construction information;

FIG. 7 is a diagram showing an example of image information includingthe construction notice sign as a construction symbol in an imagingarea;

FIGS. 8A and 8B are explanatory diagrams illustrating an example of amethod of obtaining construction information;

FIG. 9 is a table showing an example of construction information storedin a construction database;

FIGS. 10A to 10C are explanatory diagrams illustrating an overview offeature learning processing based on an image recognition result of afeature;

FIG. 11 is a partially enlarged diagram of a learning value stored in alearning database;

FIG. 12 is a flowchart showing the entire procedure of vehicle positioncorrection/feature learning processing according to the embodiment ofthe present invention;

FIG. 13 is a flowchart showing a procedure of vehicle positioncorrection processing; and

FIG. 14 is a flowchart showing a procedure of feature learningprocessing.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Next, an embodiment of the present invention will be explained based onthe drawings. FIG. 1 is a block diagram showing a schematic structure ofa navigation device 1 according to this embodiment. FIG. 2 is a diagramshowing a vehicle C in which this navigation device 1 is mounted. Thisnavigation device 1 is for being mounted in a vehicle, and includes avehicle position recognizing device 2 and a feature informationcollecting device 3 according to the present invention. This navigationdevice 1 obtains image information G of the surrounding area of thevehicle C to image recognize a feature such as a road marking (paintingor the like), and checks the image recognition result thereof withfeature information F about the feature stored in a feature database DB2to correct vehicle position information P. Accordingly, the navigationdevice 1 can obtain highly precise vehicle position information P, andprovide guidance more appropriately. However, when traveling on a roadwhere construction is performed, there may be a case that the vehicle Chas to travel on an opposite lane, a case that a feature such as a roadmarking is moved or rewritten, or the like. Thus, it often happens thatcorrection of the vehicle position information P cannot be performedappropriately. Accordingly, the navigation device 1 has a function tostop, when construction information W is obtained, correction of thevehicle position information P within a construction section thereof.When performing correction of the vehicle position information P andstopping the correction in this manner, the navigation device 1functions as the vehicle position recognizing device 2.

Further, this navigation device 1 is arranged to store the obtainedconstruction information W in a construction database DB3. Then, if theconstruction has been completed when the vehicle C travels on thesection of a road corresponding to the construction section according tothe already stored construction information W, the device has a functionto perform image recognition processing of a feature in the constructionsection according to the construction information W, and generate, basedon the image recognition result thereof and the vehicle positioninformation P, learned feature information Fb as feature information Fof the image-recognized feature. Accordingly, a feature such as a roadmarking that is moved or rewritten due to construction can be learnedappropriately, and feature information F of the learned feature can beused for correcting the vehicle position information P. When learning afeature based on the existing construction information W in this manner,the navigation device 1 functions as the feature information collectingdevice 3.

The navigation device 1 shown in FIG. 1 includes, as functional units,an image information obtaining unit 13, a vehicle position informationobtaining unit 14, a feature image recognizing unit 18, a vehicleposition correcting unit 19, a navigation calculation unit 20, anexternal information receiving unit 32, a construction image recognizingunit 33, a construction information generating unit 34, a correctionstop processing unit 35, a construction completion determining unit 36,a construction information deletion determining unit 37, a target typedetermining unit 42, a recognized feature information generating unit43, an estimated position determining unit 46, and a learned featureinformation generating unit 47. These functional units are implementedby hardware or software (program) or both of them in order to performvarious processing on inputted data, with an arithmetic processingdevice, such as a CPU, as a core member that is commonly orindependently used. Further, these functional units are configured to beable to transfer information to each other.

A map database DB1 includes, for example, a device having a recordingmedium capable of storing information and a driving unit thereof as ahardware configuration, such as a hard disk drive, a DVD drive having aDVD-ROM, or a CD drive having a CD-ROM. Further, the feature databaseDB2, the construction database DB3, and a learning database DB4 include,for example, a device having a recording medium capable of storing andrewriting information and a driving unit thereof as a hardwareconfiguration, such as a hard disk drive, a flash memory, or the like.Hereinafter, structures of the units of the navigation device 1according to this embodiment will be explained in detail.

1. Map Database

The map database DB1 is a database that stores map information M dividedinto predetermined sections. FIG. 3 is a diagram showing an example of astructure of the map information M stored in the map database DB1. Asshown in this diagram, the map information M includes road information Rrepresenting a road network by a connection relationship of a pluralityof links k. The road network is constituted of links k and nodes n eachbeing a connection point of two links k. The node n corresponds to anintersection on an actual road, and the link k corresponds to a roadconnecting intersections. Each node n has information of a position(coordinates) on a map represented by latitude and longitude. Each linkk has information such as load length, road type, road width, number oflanes, and shape interpolation point for representing a link shape, aslink attribute information. Here, the road type is information of a roadtype when roads are categorized into a plurality of types, such asexpressway, national highway, prefectural highway, open road, narrowroad, and introducing road for example. The node n has information oftraffic restriction, presence of a signal, and the like as nodeattribute information. Note that, in FIG. 3, only the road information Rof one section is shown, and road information R for other sections areomitted.

This road information R is used for map matching, route search from adeparture point to a destination, route guidance to the destination, andthe like, which will be described later. Besides them, the mapinformation M that includes the road information R is used fordisplaying a map of the surrounding area of the vehicle C, thesurrounding area of a destination, and the like, displaying a route to adestination, and the like. Accordingly, the map information M includes,although not shown, drawing information having various informationnecessary for displaying a map, intersection information constituted ofdetailed information of intersections, and the like, in addition to theroad information R as described above. Further, this drawing informationincludes background information necessary for displaying road shapes,buildings, rivers, and the like, character information necessary fordisplaying city, town, and village names as well as road names, and thelike.

2. Feature Database

The feature database DB2 is a database that stores information of aplurality of features provided on a road or the surrounding area of aroad, namely, feature information F. As shown in FIG. 1, in thisembodiment, the feature database DB2 stores two types of information,i.e., initial feature information Fa and learned feature information Fb.Here, the initial feature information Fa is feature information F abouta plurality of features prepared and stored in advance in the featuredatabase DB2. On the other hand, the learned feature information Fb is,as will be described later, feature information F generated by thelearned feature information generating unit 47 as a result of learningusing an image recognition result of a feature by the feature imagerecognizing unit 18 and stored in the feature database DB2. Note that inthe explanation below, when “feature information F” is mentioned simply,it generically refers to these initial feature information Fa andlearned feature information Fb. Note that, in this embodiment, theinitial feature information Fa and the learned feature information Fbare stored in the same feature database DB2, but they may be stored inseparated databases.

Features for which feature information F is stored in this featuredatabase DB2 include a road marking (painting) provided on the surfaceof a road. FIG. 4 is a diagram showing an example of the featureinformation F of a road marking stored in the feature database DB2.Examples of the feature related to such a road marking include acrosswalk, a stop line, a speed marking representing a maximum speed orthe like, a zebra zone, a lane marking (including various lane markingssuch as a solid line, a broken line, a double line, and the like)sectioning each lane along the road, a traffic section marking for eachtraveling direction (including an arrow marking, for example, astraight-ahead arrow, a right-turn arrow, and the like) specifying atraveling direction of each lane, and the like. In addition to such roadmarkings, examples of the feature whose feature information F is storedcan include various features such as a traffic signal, a sign, anoverpass, a tunnel, and a manhole.

Further, the feature information F includes, as contents thereof,position information of each feature, and feature attribute informationrelated thereto. Here, the position information includes a position(coordinates) on a map of a representative point of each feature relatedto a link k, a node n, and the like constituting road information R, andinformation of the direction of each feature. In this example, therepresentative point is set in the vicinity of a center portion in alength direction and a width direction of each feature. Further, featureattribute information includes identification information foridentifying a feature from other features (feature ID), type informationrepresenting a feature type of each feature, feature form information ofthe shape, size, color, and the like of the feature. Here, the featuretype is information representing the type of a feature having basicallythe same form such as, specifically, “crosswalk”, “stop line”, or “speedmarking (30 km/hour)”.

3. Image Information Obtaining Unit

The image information obtaining unit 13 functions as an imageinformation obtaining unit which obtains image information G of thesurrounding area of the vehicle C captured by an imaging device. Here,the imaging device is an on-vehicle camera or the like having an imagingelement, and is preferable if the imaging device is provided at least ata position where it can image the surface of a road in the surroundingarea of the vehicle C. In this embodiment, as the imaging device, asshown in FIG. 2, there are provided a back camera 11 which captures aroad surface on a rear side of the vehicle C, and a front camera 12which captures a road surface and further an upper side of a front sideof the vehicle C. In this example, the back camera 11 is provided tocapture a lower side (road surface side) than the front camera 12.Therefore, the back camera 11 can capture a road surface closer to thevehicle C than the front camera 12. The image information obtaining unit13 takes in image information G captured by the back camera 11 and thefront camera 12 at predetermined time intervals via a frame memory (notshown). A time interval for taking in the image information G at thistime can be about, for example, 10 to 50 ms. Accordingly, the imageinformation obtaining unit 13 can successively obtain image informationG of a plurality of frames captured by the back camera 11 and the frontcamera 12. The image information G obtained here is output to thefeature image recognizing unit 18. Note that, in the explanation below,when image information G is simply mentioned, it includes both the imageinformation G captured by the back camera 11 and the image information Gcaptured by the front camera 12.

4. Vehicle Position Information Obtaining Unit

The vehicle position information obtaining unit 14 functions as avehicle position information obtaining unit which obtains vehicleposition information P representing the current position of the vehicleC. Here, the vehicle position information obtaining unit 14 is connectedto a GPS receiver 15, a direction sensor 16, and a distance sensor 17.Here, the GPS receiver 15 is a device receiving a GPS signal from aglobal positioning system (GPS) satellite. This GPS signal is normallyreceived at every second and outputted to the vehicle positioninformation obtaining unit 14. In the vehicle position informationobtaining unit 14, the signal from the GPS satellite received by the GPSreceiver 15 is analyzed, and thereby information such as the currentposition (latitude and longitude), the traveling direction, and movingspeed of the vehicle C can be obtained. The direction sensor 16 is asensor that detects the traveling direction or a change in the travelingdirection of the vehicle C. This direction sensor 16 is constituted of,for example, a gyro sensor, a geomagnetism sensor, an optical rotationsensor or a rotation-type resistance volume attached to a rotation partof a steering wheel, an angle sensor attached to a wheel part, etc.Then, the direction sensor 16 outputs a detection result thereof to thevehicle position information obtaining unit 14. The distance sensor 17is a sensor that detects a vehicle speed and a moving distance of thevehicle C. This distance sensor 17 is constituted of, for example, avehicle speed pulse sensor that outputs a pulse signal every time adrive shaft, a wheel, or the like of the vehicle rotates by a certainamount, a yaw/G sensor that detects acceleration of the vehicle C, acircuit that integrates the detected acceleration, etc. Then, thedistance sensor 17 outputs information of vehicle speed and movingdistance as detection results thereof to the vehicle positioninformation obtaining unit 14.

Then, the vehicle position information obtaining unit 14 performscalculation for identifying the position of the vehicle C by a knownmethod based on outputs from these GPS receiver 15, direction sensor 16and distance sensor 17. The vehicle position information P obtained inthis manner results in information including errors due to detectionaccuracies of the sensors 15 to 17. Accordingly, in this embodiment, thevehicle position information obtaining unit 14 obtains road informationR of the surrounding area of the vehicle position from the map databaseDB1, and performs publicly known map matching based on this information,thereby performing correction to match the vehicle position with a linkk or a node n included in the road information R. Further, by thevehicle position correcting unit 19 which will be described later, theposition of the vehicle C in the traveling direction shown in thevehicle position information P is corrected using the image informationG and the feature information F. Accordingly, the vehicle positioninformation obtaining unit 14 obtains highly precise vehicle positioninformation P of the vehicle C.

5. Feature Image Recognizing Unit

The feature image recognizing unit 18 functions as a feature imagerecognizing unit which performs image recognition processing of afeature included in image information G obtained by the imageinformation obtaining unit 13. In this embodiment, the feature imagerecognizing unit 18 performs two types of image recognition processing,i.e., image recognition processing for position correction forcorrecting the vehicle position information P by the vehicle positioncorrecting unit 19 which will be described later, and image recognitionprocessing for feature learning for generating the learned featureinformation Fb by a feature learning unit 41. That is, the navigationdevice 1 performs, as will be described later, vehicle positioncorrection processing by the vehicle position correcting unit 19 basedon existing construction information W stored in the constructiondatabase DB3 and construction information W newly obtained by aconstruction information obtaining unit 31, or performs feature learningprocessing by the feature learning unit. Therefore, the feature imagerecognizing unit 18 performs the image recognition processing forposition correction when the navigation device 1 performs vehicleposition correction processing, and performs the image recognitionprocessing for feature learning when the navigation device 1 performsfeature learning processing. Note that, in this embodiment, the featureimage recognizing unit 18 is arranged to perform image recognitionprocessing of a feature, using, as a target, the image information Gobtained by the back camera 11 which is capable of capturing a roadsurface closer to the vehicle C than the front camera 12. In addition,it is also possible of course to perform the image recognitionprocessing of a feature using the image information G obtained by thefront camera 12 as a target.

In the image recognition processing for position correction, the featureimage recognizing unit 18 obtains feature information F of one, two ormore features existing in the surrounding area of the vehicle C from thefeature database DB2 based on the vehicle position information P, andperforms, with the one, two or more features being a target feature ft(refer to FIG. 5), image recognition processing of the target feature ftincluded in the image information G. In this embodiment, based on thevehicle position information P and the position information of a featureincluded in the feature information F, the feature image recognizingunit 18 obtains feature information F of one feature existing in thetraveling direction of the vehicle C from the feature database DB2, andsets the feature included in this feature information F as the targetfeature ft. Next, based on attribute information included in the featureinformation F for this target feature ft, image recognition processingis executed using a feature of the feature type indicated by thisattribute information as a target. In this manner, by narrowing thetarget feature to a feature of one feature type, it is possible tosuppress erroneous recognition and increase the accuracy of imagerecognition processing. This image recognition processing for positioncorrection is performed based on position information of this targetfeature ft included in the feature information F and targeting apredetermined recognition section set to the surrounding area of thetarget feature ft. Then, the image recognition result of the targetfeature ft by this image recognition processing for position correctionis used for correcting the vehicle position information P by the vehicleposition correcting unit 19.

In the image recognition processing for feature learning, the featureimage recognizing unit 18 performs, using a feature of one, two or moretarget types determined by the target type determining unit 42 as atarget, which will be described later, image recognition processing of afeature of this one, two or more target types included in the imageinformation G. This determination of a target type by the target typedetermining unit 42 is performed referring to the initial featureinformation Fa stored in the feature database DB2, and this point willbe described later. This image recognition processing for featurelearning is performed, as will be described later, based on existingconstruction information W stored in the construction database DB3 andtargeting a construction section according to this constructioninformation W. That is, based on the vehicle position information P, thefeature image recognizing unit 18 executes image recognition processingof a feature of the target type included in the image information Gwhile the vehicle C travels the section of a road corresponding to theconstruction section according to the existing construction informationW stored in the construction database DB3. Then, the image recognitionresult of a feature of this image recognition processing for featurelearning is used for obtaining recognition position information andfeature attribute information of a feature by the recognized featureinformation generating unit 43 of the feature learning unit 41.

The feature image recognizing unit 18 performs binarization processing,edge detection processing, and the like on image information G duringimage recognition of a feature in the image recognition processing forposition correction and the image recognition processing for featurelearning, and extracts contour information of a feature (road marking)included in this image information G. Thereafter, the feature imagerecognizing unit 18 performs pattern matching of the extracted contourinformation of the feature with the characteristic amount of the form ofthe target feature or a feature of the target type. Then, if the patternmatching has succeeded, it is determined that image recognition of thisfeature has succeeded, and the image recognition result thereof isoutputted. The output destination of this image recognition result isthe vehicle position correcting unit 19 in the case of the imagerecognition processing for position correction, and the recognizedfeature information generating unit 43 in the case of the imagerecognition processing for feature learning. On the other hand, whenpattern matching has not succeeded in a section where image recognitionprocessing of this feature is performed, that is, the recognitionsection in the case of the image recognition processing for positioncorrection or the construction section in the case of the imagerecognition processing for feature learning, it is determined that theimage recognition of this feature is failed. In this case, informationindicating that the image recognition is failed is outputted to thevehicle position correcting unit 19 or the recognized featureinformation generating unit 43.

6. Vehicle Position Correcting Unit

The vehicle position correcting unit 19 functions as a vehicle positioncorrecting unit which checks an image recognition result of a feature bythe feature image recognizing unit 18 with the feature information F ofthis feature so as to correct the vehicle position information P. Inthis embodiment, the vehicle position correcting unit 19 corrects thevehicle position information P in the traveling direction of the vehicleC along a link k of the road information R. Specifically, based on animage recognition result of a target feature ft by the image recognitionprocessing for position correction of the feature image recognizing unit18, and an attaching position, an attaching angle, an angle of view, andthe like of the back camera 11 as an imaging device, the vehicleposition correcting unit 19 first calculates a positional relationshipbetween the vehicle C and the target feature ft at the time of obtainingthe image information G including an image of the target feature ft. Forexample, when the image information G is obtained in the situation shownin FIG. 5, the vehicle position correcting unit 19 calculates apositional relationship (for example, a distance d) between the vehicleC and a crosswalk as the target feature ft based on the imagerecognition result of the image information G. Next, based on thecalculation result of this positional relationship between the vehicle Cand the target feature ft and the position information of the targetfeature ft included in feature information F obtained from the featuredatabase DB2, the vehicle position correcting unit 19 calculates andobtains highly precise position information of the vehicle C withreference to the position information (feature information F) of thetarget feature ft in the traveling direction of the vehicle C. Then,based on the highly precise position information of the vehicle Cobtained in this manner, the vehicle position correcting unit 19corrects information of the current position in the traveling directionof the vehicle C included in the vehicle position information P obtainedby the vehicle position information obtaining unit 14. As a result, thevehicle position information obtaining unit 14 obtains highly precisevehicle position information P after being corrected.

7. Navigation Calculation Unit

The navigation calculation unit 20 is a calculation processing unit thatoperates in accordance with an application program 21 for executing anavigation function such as vehicle position display, route search froma departure point to a destination, route guidance to the destination,destination search, and the like. Here, referring to the vehicleposition information P, the map information M including the roadinformation R, the feature information F etc., the application program21 causes the navigation calculation unit 20 to execute variousnavigation functions. For example, the navigation calculation unit 20obtains the map information M of the surrounding area of the vehicle Cfrom the map database DB1 based on the vehicle position information P soas to display an image of the map on the display screen of a displayinput device 22, and performs processing of displaying a vehicleposition mark to overlap on the image of this map based on the vehicleposition information P. Further, the navigation calculation unit 20performs route search from a predetermined departure point to adestination based on the map information M stored in the map databaseDB1. Moreover, the navigation calculation unit 20 performs routeguidance for the driver using one or both of the display input device 22and an audio output device 23 based on the searched route from thedeparture point to the destination and the vehicle position informationP. When executing these navigation functions, the navigation calculationunit 20 can perform more appropriate guidance since the highly precisevehicle position information P can be obtained by the vehicle positioncorrecting unit 19 as described above. Note that, in the display inputdevice 22, a display device such as a liquid crystal display device, andan input device such as a touch panel or an operation switch areintegrated. The audio output device 23 includes a speaker and the like.In this embodiment, the navigation calculation unit 20, the displayinput device 22, and the audio output device 23 function as a guidanceinformation output unit 24 in the present invention.

8. External Information Receiving Unit

The external information receiving unit 32 functions as a constructioninformation receiving unit which receives construction information Wfrom a transmitter disposed outside of the vehicle C. In thisembodiment, the external information receiving unit 32 is constituted ofa device that receives vehicle information and communication system(VICS) information. Therefore, examples of the transmitter disposedoutside of the vehicle C include a radio wave beacon transmitter, anoptical beacon transmitter, an FM multiplexed broadcast transmitter, andthe like, which constitute the VICS. As already known, road trafficinformation supplied from the VICS includes construction information Wabout the road on which the vehicle C is traveling and roads in thesurrounding area. This construction information W includes informationof a construction section of the construction. Then, upon reception ofconstruction information W from these transmitters, the externalinformation receiving unit 32 outputs this construction information W tothe correction stop processing unit 35, which will be described later.Further, the external information receiving unit 32 stores the receivedconstruction information W in the construction database DB3. Theconstruction information W received by this external informationreceiving unit 32 is stored in the third row in the constructiondatabase DB3 in the example shown in FIG. 9. In addition, the externalinformation receiving unit 32 may be a device that receives road trafficinformation and construction information from other than the VICS.Specifically, it is also preferable that the external informationreceiving unit 32 is constituted of a device that receives informationfrom a system that distributes road traffic information, constructioninformation, and the like to the navigation device 1 or the like usingvarious wireless communication networks, for example, a cellular phonenetwork. Further, the area of receiving the construction information Wis not limited to the road on which the vehicle C is traveling or otherroads in the surrounding area, and it is also preferable that the deviceis structured to receive all the construction information W of theregion in which the vehicle C exists or in the country for example.

9. Construction Image Recognizing Unit

The construction image recognizing unit 33 functions as a constructionimage recognizing unit which performs image recognition processing of aconstruction symbol wt (refer to FIG. 7) included in image information Gobtained by the image information obtaining unit 13. Here, theconstruction symbol wt is one of various objects provided in a sitewhere road construction is performed, and is preferable if it is anobject having a characteristic form that can be image recognized easily.Examples of such a construction symbol wt include a construction noticesign, a construction fence, a construction barricade, a security light,a cone, a construction guide human model (for example, a human modelimitating a security guard), and the like, and the construction imagerecognizing unit 33 takes at least one of them as a target of imagerecognition processing. The construction notice sign, the constructionfence, the construction barricade, and the like are preferable whenimage recognition processing is performed thereon taking a pattern suchas oblique lines of yellow and black, a typical outline and/or the like,which are frequently used for them, as a characteristic amount. Further,the security light, the cone, the construction guide human model, or thelike often has a characteristic outline, and thus it is preferable touse the outline as a characteristic amount for performing imagerecognition processing. In this embodiment, the construction imagerecognizing unit 33 performs image recognition processing of theconstruction symbol wt, using the image information G obtained by bothof the back camera 11 and the front camera 12 as a target.

When image recognizing a construction symbol wt, the construction imagerecognizing unit 33 performs binarization processing, edge detectionprocessing, and/or the like on image information G, and extracts contourinformation of the construction symbol wt included in this imageinformation G. Thereafter, the construction image recognizing unit 33performs pattern matching of the extracted contour information of theconstruction symbol wt with characteristic amounts of the forms of aplurality of types of construction symbols wt prepared in advance. Then,if the pattern matching has succeeded with the characteristic amount ofthe form of one of the construction symbols wt, it is determined thatthis construction symbol wt is image-recognized, and the imagerecognition result thereof is outputted to the construction informationgenerating unit 34. The construction image recognizing unit 33 executessuch image recognition processing of a construction symbol wt for allimage information G or image information G at a predetermined intervalobtained by both of the back camera 11 and the front camera 12. Further,when a construction notice sign is image-recognized as a constructionsymbol wt, the construction image recognizing unit 33 executes imagerecognition processing of characters included in this constructionnotice sign. Accordingly, information of a construction section, aconstruction period, and the like described on the construction noticesign can be obtained. Note that, since numerous known technologiesalready exist for a specific method of image recognition processing ofcharacters, explanation thereof is omitted here. The thus obtainedinformation of the type of the construction symbol wt for which imagerecognition has succeeded, and the information of the recognition resultof the characters in a case where the construction symbol wt is aconstruction notice sign are outputted to the construction informationgenerating unit 34 as image recognition results by the constructionimage recognizing unit 33.

FIGS. 6A and 6B are explanatory diagrams illustrating examples of anobtaining method of construction information W. FIG. 6A shows an exampleof a road condition when construction is performed on the road on whichthe vehicle C is traveling, and FIG. 6B shows contents of theconstruction information W obtained in the road condition shown in FIG.6A. In the condition shown in FIG. 6A, a construction notice sign isdisposed as a construction symbol wt ahead of the vehicle C, andconstruction to dig up the road is performed for a predetermineddistance behind the construction notice sign. FIG. 7 shows an example ofimage information G including the construction notice sign as aconstruction symbol wt in an imaging area. This diagram corresponds toimage information G obtained by the front camera 12 of the vehicle C ata position shown in FIG. 6A. The construction notice sign shown in theexample of FIG. 7 includes thereon, besides character information “underroad construction”, character information “200 m from here” indicating aconstruction section, and character information “from January 8 toJanuary 25” indicating a construction period. Therefore, theconstruction image recognizing unit 33, when it performed imagerecognition processing using the image information G shown in FIG. 7 asa target, recognizes the image of the construction notice sign as aconstruction symbol wt and executes character image recognition toobtain the information of the construction section and the constructionperiod. These pieces of information are outputted as image recognitionresults to the construction information generating unit 34

FIGS. 8A and 8B are, similarly to FIGS. 6A and 6B, explanatory diagramsillustrating examples of an obtaining method of construction informationW. However, in the condition shown in these diagrams, a plurality ofsecurity lights and construction barricades are disposed alternately asconstruction symbols wt ahead of the vehicle C, and road construction isperformed in the area surrounded by them. In such a condition, theconstruction image recognizing unit 33 recognizes the images of thesecurity lights and the construction barricades as construction symbolswt included in image information G obtained first by the front camera12, and then recognizes the images of these construction symbols wtincluded in both image information G obtained by the front camera 12 andthe back camera 11 respectively. Thereafter, the construction imagerecognizing unit 33 does not recognize any image of the constructionsymbol wt in the image information G obtained by the front camera 12,and finally does not recognize any image of the construction symbol wtin both the image information G obtained by the front camera 12 and theback camera 11 respectively. Image recognition results of the imageinformation G obtained by these front camera 12 and back camera 11respectively are outputted to the construction information generatingunit 34.

10. Construction Information Generating Unit

The construction information generating unit 34 functions as aconstruction information generating unit which generates constructioninformation W based on an image recognition result of a constructionsymbol wt by the construction image recognizing unit 33. Theconstruction information W includes at least information of aconstruction section, and includes information of a construction periodin some cases. FIG. 9 is a diagram showing an example of constructioninformation W stored in the construction database DB3. As shown in thisdiagram, in this embodiment, the construction information W includesinformation of an obtained date and time and recognized type besides aconstruction section and a construction period. Here, the obtained dateand time indicates date and time of obtaining construction informationW, and the recognized type indicates the type of the construction symbolwt that is image-recognized by the construction image recognizing unit33. Incidentally, the recognized type is not limited to one, andinformation of a plurality of recognized types can be included in theconstruction information W as shown in FIG. 9. That is, there are manycases where a plurality of construction symbols wt are image-recognizedin the same construction section, and in such cases, types of theplurality of construction symbols wt are included in the constructioninformation W as information of recognized types. Further, in theexample of FIG. 9, the recognized type being “construction informationreceived” represents construction information W received by the externalinformation receiving unit 32. The construction information generatingunit 34 generates the construction information W based on the imagerecognition result of a construction symbol wt by the construction imagerecognizing unit 33. Then, the construction information generating unit34, when it generated construction information W, transmits thisconstruction information W to the correction stop processing unit 35,which will be described later. Further, the construction informationgenerating unit 34 stores the generated construction information W inthe construction database DB3. In this embodiment, this constructiondatabase DB3 corresponds to a construction information storage unit inthe present invention.

Next, a specific method of generating construction information W by theconstruction information generating unit 34 will be explained. When aconstruction symbol wt of a construction notice sign as shown in FIG. 7is image-recognized by the construction image recognizing unit 33, theconstruction information generating unit 34 receives, together withinformation about that the construction notice sign is image-recognizedas a construction symbol wt as described above, information of aconstruction section “200 m from here” and information of a constructionperiod “from January 8 to January 25” as image recognition results fromthe construction image recognizing unit 33. In this case, theconstruction information generating unit 34 sets information of theconstruction section included in the construction information Waccording to the information of the construction section as an imagerecognition result. That is, the construction information generatingunit 34 uses the vehicle position information P and the road informationR at the time of receiving this image recognition result so as to derivecoordinates of a start point (x1, y1) and an end point (x2, y2) of aconstruction section located on a link k as shown in FIG. 6B, and takesthe coordinate information “(x1, y1) to (x2, y2)” thereof as informationof a construction section as construction information W. At this time,based on the image recognition result of the construction notice sign, apositional relationship between the vehicle C and the constructionnotice sign is calculated, and the position of the construction noticesign is taken as the start point of the construction section. Inaddition, it is also preferable that a position indicated by the vehicleposition information P at the time of receiving this image recognitionresult is taken as the start point of the construction section. On theother hand, as the end point of the construction section, there is takena point at a distance (here 200 m), which is indicated by theinformation of construction section as an image recognition result,forward of the vehicle C in the traveling direction along the link kfrom the start point of the construction section.

Further, the construction information generating unit 34 generatesinformation of a construction period as construction information W basedon the date and time of receiving this image recognition result andinformation of a construction period as the image recognition result. Inthis example, the information of the image recognition result “fromJanuary 8 to January 25” is complemented by the information of the yearat the time receiving this image recognition result, thereby providing“01/08/2008 to 01/25/08” as information of a construction period asconstruction information W. Further, the construction informationgenerating unit 34 generates information of obtained date and time asconstruction information W from information of date and time which thenavigation device 1 has at the time of receiving this image recognitionresult. Further, the construction information generating unit 34generates information of a recognized type as construction information Wfrom information of the type of a construction symbol wt for which imagerecognition is succeeded included in the information of the imagerecognition result. The construction information W generated as above isstored in the first row of the construction database DB3 in the exampleshown in FIG. 9.

On the other hand, when the construction image recognizing unit 33recognizes the image of the construction notice sign which has noinformation of a construction section, a construction period and thelike, or from which they cannot be image-recognized, or when any otherconstruction symbol wt such as a construction fence, a constructionbarricade, a security light, a cone, a construction guide human model,or the like is image-recognized, the construction information generatingunit 34 generates construction information W by a method partiallydifferent from the above. For example, when a construction symbol wtwhich is disposed alone, such as a construction notice sign, a securitylight, or a construction guide human model is image-recognized, theconstruction information generating unit 34 sets a predetermined sectionwith reference to the recognition position of this construction symbolwt as information of a construction section included in constructioninformation W. In this case, it is preferable that the predeterminedsection is set to, for example, a predetermined distance forward in thetraveling direction of the vehicle C along a link k from the disposedposition of the construction symbol wt or a predetermined distanceforward and backward in the traveling direction of the vehicle C alongthe link k from the disposed position of the construction symbol wt.Here the predetermined distance may be a fixed value or a variable valueto be changed based on various information shown in an image recognitionresult of the construction symbol wt. Also in this example, theinformation of the obtained date and time and the recognized type can begenerated similarly to the above example in which the image of theconstruction notice sign as shown in FIG. 7 is recognized. On the otherhand, in this example, the information on the construction period is notgenerated, and the construction information W does not includeinformation on the construction period.

Further, as shown in FIGS. 8A and 8B for example, when constructionsymbols wt disposed to surround a road construction site, such as aconstruction fence, a construction barricade, a cone, and a plurality ofsecurity lights are image-recognized, the construction symbols wt areincluded in image information G of a plurality of consecutive frames. Inthis case, the construction information generating unit 34 combines animage recognition result of image information G of a front side of thevehicle C obtained by the front camera 12 by the construction imagerecognizing unit 33 and an image recognition result of image informationG of a rear side of the vehicle C obtained by the back camera 11 togenerate information of the construction section. That is, theconstruction information generating unit 34 sets the start point of theconstruction section with reference to the recognition position of thefirst construction symbol wt included in the image information G of thefront side of the vehicle C, and sets the end point of the constructionsection with reference to the recognition position of the lastconstruction symbol wt included in the image information G of the rearside of the vehicle C. Specifically, the construction informationgenerating unit 34 first sets the start point of the constructionsection based on an image recognition result of a construction symbol wtfor image information G of a construction symbol wt first captured bythe front camera 12 and vehicle position information P at the time ofreceiving this image recognition result from the construction imagerecognizing unit 33. Thereafter, the construction information generatingunit 34 sets the end point of the construction section based on an imagerecognition result of a construction symbol wt for image information Gof a construction symbol wt last captured by the back camera 11 andvehicle position information P at the time of receiving this imagerecognition result from the construction image recognizing unit 33.Here, the first and last construction symbols are, for example, thefirst captured construction symbol wt and the last captured constructionsymbol wt in a case where a plurality of construction symbols wtdisposed to surround the road construction site are captured acrossimage information G of a plurality of consecutive frames by the frontcamera 12 or the back camera 11. At this time, it is preferable that thepositional relationship between the vehicle C and a construction symbolwt is calculated based on the respective image recognition results, andthe position of the construction symbol wt is taken as the start or endpoint of the construction section, or the position indicated by thevehicle position information P at the time of receiving the respectiveimage recognition results is taken as the start or end point of theconstruction section. Also in this example, information of the obtaineddate and time and the recognized type can be generated similarly to theexample in which the image of the construction notice sign as shown inFIG. 7 above is recognized. On the other hand, in this example,information of the construction period is not generated, and theconstruction information W does not include information of theconstruction period. The construction information W generated asdescribed above is stored in the second row in the construction databaseDB3 in the example shown in FIG. 9.

Further, for example, in the case where only one of the front camera 12and the back camera 11 is used for image recognition processing of aconstruction symbol wt (refer to FIG. 7) and construction symbols wt areincluded in the image information G of a plurality of consecutive framesobtained by this one of the imaging devices 12, 11, information of theconstruction section is generated based on the image recognition resultsthereof. This case is also basically similar to the example according toFIGS. 8A and 8B, where the construction information generating unit 34sets the start point of the construction section with reference to therecognition position of the first construction symbol wt included in theobtained image information Q and sets the end point of the constructionsection with reference to the recognition position of the lastconstruction symbol wt included in the image information G. Also in thiscase, it is preferable that the positional relationship between thevehicle C and the construction symbol wt is calculated based on therespective image recognition results, and the position of theconstruction symbol wt is taken as the start or end point of aconstruction section, or the position indicated by the vehicle positioninformation P at the time of receiving the respective image recognitionresults is taken as the start or end point of the construction section.

As described above, the construction information W including informationof the construction section for the road on which the vehicle C istraveling is obtained by the external information receiving unit 32 orby the construction image recognizing unit 33 and the constructioninformation generating unit 34. Therefore, in this embodiment, theexternal information receiving unit 32, the construction imagerecognizing unit 33, and the construction information generating unit 34correspond to the construction information obtaining unit 31 in thepresent invention.

11. Correction Stop Processing Unit

The correction stop processing unit 35 functions as a correction stopunit which stops correction of vehicle position information P by thevehicle position correcting unit 19 in a construction section based onconstruction information W. That is, when the correction stop processingunit 35 has obtained construction information W by the constructioninformation obtaining unit 31, the correction stop processing unit 35stops, based on information of a construction section included in thisconstruction information W and vehicle position information P,processing of the vehicle position correcting unit 19 while the positionof the vehicle C indicated by the vehicle position information P is inthe construction section. Accordingly, while traveling in a section of aroad in a state different from normal times due to road constructionbeing performed, it is possible to suppress occurrence of a situationwhere a feature different from a target feature ft indicated by thefeature information F stored in the feature database DB2 is erroneouslyimage-recognized as the target feature ft and the vehicle positioninformation P is erroneously corrected. Further, here the correctionstop processing unit 35 also stops other processing related tocorrection of the vehicle position information P by the vehicle positioncorrecting unit 19. Specifically, the correction stop processing unit 35stops the image recognition processing for position correction by thefeature image recognizing unit 18. Accordingly, the calculationprocessing load for correction processing of the vehicle positioninformation P can be eliminated, and the calculation processing load forimage recognition processing can be eliminated. Thus, the calculationprocessing load on the entire navigation device 1 can be reduced.

Further, in this embodiment, the correction stop processing unit 35performs processing of stopping the correction of the vehicle positioninformation P by the vehicle position correcting unit 19 even whenconstruction information W is not obtained by the constructioninformation obtaining unit 31. Specifically, the correction stopprocessing unit 35 stops the function of the vehicle position correctingunit 19 also when the vehicle C travels the section of a roadcorresponding to a construction section according to existingconstruction information W stored in the construction database DB3 anduntil generation of learned feature information Fb is completed in thissection by the feature learning unit 41, which will be described later.Accordingly, when construction according to existing constructioninformation W is completed and while the vehicle C travels the sectionof a road corresponding to the construction section, it is possible toprevent occurrence of a situation where the vehicle position informationP is erroneously corrected based on feature information F before theconstruction.

The construction information obtaining unit 31 and the correction stopprocessing unit 35 as has been explained are functional units thatfunction to limit the function of the vehicle position correcting unit19 when the vehicle C obtains new construction information W whiletraveling. Then, the newly obtained construction information W is storedand retained in the construction database DB3 as described above. Theconstruction information W stored in the construction database DB3 inthis manner is used for determining whether or not feature learningprocessing is executed for learning a feature which has a possibility ofbeing changed by construction when the vehicle C travels in the samesection of the road next time. Functional units that function to performthe feature learning processing using the existing constructioninformation W stored in the construction database DB3 will be explainedbelow.

12. Construction Completion Determining Unit

When the vehicle C travels the section of a road corresponding to aconstruction section according to the construction information W alreadystored in the construction database DB3, the construction completiondetermining unit 36 functions as a construction completion determiningunit which determines completion of the construction indicated by theconstruction information W. Here, based on information of a constructionsection included in the existing construction information W stored inthe construction database DB3 as shown in FIG. 9 and the vehicleposition information P obtained by the vehicle position informationobtaining unit 14, the construction completion determining unit 36determines whether or not the vehicle C travels the section of the roadcorresponding to the construction section according to the constructioninformation W. In this embodiment, the construction completiondetermining unit 36 performs this determination when the position of thevehicle C indicated by the vehicle position information P enters thesection of the road corresponding to the construction section indicatedby the construction information W. Then, when it determined that thevehicle C travels the section of the road corresponding to theconstruction section according to the existing construction informationW, the construction completion determining unit 36 determines whether ornot the construction indicated by this construction information W iscompleted.

In this embodiment, the construction completion determining unit 36determines whether or not the construction according to the existingconstruction information W is completed using the following method.Specifically, when the vehicle C travels the section of the roadcorresponding to the construction section according to the existingconstruction information W, the construction completion determining unit36 determines that the construction indicated by this existingconstruction information W is completed when construction information Wincluding the same construction section is not obtained by theconstruction information obtaining unit 31. This is because, whenconstruction information W having information of the same constructionsection as in the existing construction information W is not obtained,it can be determined that the construction in this construction sectionis completed. Further, when the existing construction information Wincludes information of a construction period, if the constructionperiod according to this construction information W is over when thevehicle C travels the section of the road corresponding to theconstruction section according to the existing construction informationW, the construction completion determining unit 36 determines that theconstruction indicated by this construction information W is completed.This is because, when the construction information W includesinformation of a construction period, the completion of construction canbe determined based on this information.

When it determined that construction according to existing constructioninformation W is completed, the construction completion determining unit36 outputs information indicating this to the target type determiningunit 42, which will be described later. Accordingly, the target typedetermining unit 42 determines a target type which is a feature typeused as a target for the image recognition processing for featurelearning. Next, when the vehicle C travels the section of a roadcorresponding to a construction section according to existingconstruction information W, the feature image recognizing unit 18executes the above-described image recognition processing for featurelearning, targeting a feature of this target type. Then, based on animage recognition result of this image recognition processing forfeature learning, the functional units of the feature learning unit 41execute the feature learning processing.

13. Construction Information Deletion Determining Unit

The construction information deletion determining unit 37 functions as aconstruction information deleting unit which deletes, when the vehicle Cdoes not travel the section of a road corresponding to a constructionsection according to the construction information W already stored inthe construction database DB3 for a predetermined period, thisconstruction information W from the construction database DB3. As shownin FIG. 9, the construction information W stored in the constructiondatabase DB3 includes information of obtained date and time. Based onthis information of date and time included in the constructioninformation W, when completion determination of this constructioninformation W is not performed by the construction completiondetermining unit 36 until a predetermined period passes since theconstruction information W has been stored in the construction databaseDB3, the construction information deletion determining unit 37 deletesthis construction information W from the construction database DB3.Accordingly, it is possible to suppress the data amount of theconstruction database DB3 from becoming excessively large. Here, thepredetermined period until deleting the construction information W maybe a fixed value, and is preferable to be set to a period larger enoughthan a normal road construction period.

14. Feature Learning Unit

The feature learning unit 41 is a unit which causes, when completion ofconstruction is determined by the construction completion determiningunit 36, the feature image recognizing unit 18 to perform imagerecognition processing of a feature in a construction section accordingto the construction information W whose completion has been determined,and generates learned feature information Fb including positioninformation and attribute information of the image-recognized featurebased on the image recognition result and the vehicle positioninformation P. In this embodiment, as shown in FIG. 1, the featurelearning unit 41 includes the target type determining unit 42, therecognized feature information generating unit 43 having a recognitionposition information obtaining unit 44 and a feature attributeinformation obtaining unit 45, the estimated position determining unit46, the learned feature information generating unit 47, and the learningdatabase DB4. Structures of the respective units of the feature learningunit 41 will be explained below.

15. The Target Type Determining Unit

The target type determining unit 42 functions as a target typedetermining unit that determines a target type which is a feature typeused as a target for the image recognition processing for featurelearning by the feature image recognizing unit 18. Here, when the targettype determining unit 42 receives from the construction completiondetermining unit 36 information indicating that construction accordingto existing construction information W stored in the constructiondatabase DB3 is completed, it determines a target type in the imagerecognition processing for feature learning which is performed targetingthe section of a road corresponding to a construction section accordingto this construction information W. In this embodiment, the target typedetermining unit 42 determines one target type for one constructionsection. At this time, the target type determining unit 42 determines atarget type having a high possibility to exist in this constructionsection as the target type. As such a target type, first, in aconstruction section according to the construction information W whosecompletion has been determined, the same target type as a feature whichexisted before the construction is highly possible. Therefore, thetarget type determining unit 42 first obtains from the feature databaseDB2 feature information F having position information in theconstruction section according to the construction information W whosecompletion has been determined, and determines the feature type, whichis the same as the feature according to the feature information F, asthe target type. Thus, when the feature image recognizing unit 18executes the image recognition processing for feature learning in theconstruction section, a feature of the same type as the featureaccording to the feature information F, having position information inthis construction section stored in the feature database DB2, can beimage-recognized with priority.

On the other hand, when the feature information F having positioninformation in the construction section does not exist in the featuredatabase DB2, the target type determining unit 42 determines a targettype having a statistically high possibility to exist as the targettype, based on a road type, a road width, the number of lanes, a linkshape, and the like which are link attribute information of a link kincluded in the road information R. Further, the target type determiningunit 42 performs processing to change the target type if it did notsucceed in image recognition of a feature of this target type even whenthe feature image recognizing unit 18 executes the image recognitionprocessing for feature learning a plurality of times for the samesection after the target type determining unit 42 has determined thetarget type once. In this case, it is preferable to determine the targettype in order from a target type having a statistically high possibilityto exist, based on link attribute information of the link k included inthe road information R. The information of the target type determined bythe target type determining unit 42 is outputted to the feature imagerecognizing unit 18 and taken as the target type for the imagerecognition processing for feature learning.

16. Recognized Feature Information Generating Unit

The recognized feature information generating unit 43 functions as arecognized feature information generating unit which generatesrecognized feature information A representing an image recognitionresult of the image recognition processing for feature learning by thefeature image recognizing unit 18. Here, the recognized featureinformation A includes recognition position information s representing arecognition position of a feature by the feature image recognizing unit18 and feature attribute information representing an attribute of thefeature. Here, the recognized feature information generating unit 43 hasthe recognition position information obtaining unit 44 for obtaining therecognition position information s included in the recognized featureinformation A, and the feature attribute information obtaining unit 45for obtaining the feature attribute information. The recognized featureinformation generating unit 43 associates the recognition positioninformation s obtained by the recognition position information obtainingunit 44 with the feature attribute information obtained by the featureattribute information obtaining unit 45 to generate recognized featureinformation A. Then, the recognized feature information generating unit43 stores the generated recognized feature information A in the learningdatabase DB4. Thus, in this embodiment, the learning database DB4corresponds to a recognition result storage unit in the presentinvention.

The recognition position information obtaining unit 44 functions as arecognition position information obtaining unit which obtains, for afeature for which image recognition has succeeded in the imagerecognition processing for feature learning by the feature imagerecognizing unit 18, recognition position information s representing arecognition position of the feature. In this embodiment, the recognitionposition information obtaining unit 44 first monitors whether or notimage recognition of a feature of the target type has succeeded in theimage recognition processing for feature learning by the feature imagerecognizing unit 18. Then, when image recognition of a feature of thetarget type has succeeded, the recognition position informationobtaining unit 44 derives a recognition position of this feature basedon the image recognition result and the vehicle position information Pobtained by the vehicle position information obtaining unit 14. Here,the recognition position information obtaining unit 44 derives thevehicle position information P at the time of obtaining imageinformation G including an image of the feature for which recognitionhas succeeded as a recognition position of this feature. Then, therecognition position information obtaining unit 44 generates therecognition position information s based on information of therecognition position of the feature derived in this manner. As will bedescribed later, in this embodiment, the recognition positioninformation obtaining unit 44 generates recognition position informations for each feature as a learning value for a predetermined positionrange to which the recognition position of the feature indicated by therecognition position information s belongs. In addition, the recognitionposition information s of the feature obtained in this manner is derivedwith reference to the vehicle position information P, and hence itserves as information of the position reflecting an error included inthe vehicle position information P.

The feature attribute information obtaining unit 45 functions as afeature attribute information obtaining unit which obtains, for afeature for which image recognition has succeeded in the imagerecognition processing for feature learning by the feature imagerecognizing unit 18, feature attribute information representing theattribute of the feature based on the image recognition result thereof.This feature attribute information constitutes a part of the recognizedfeature information A and the learned feature information Fb. Here, theattribute of the feature represented by the feature attributeinformation can identify at least the one feature from other features.Therefore, it is preferable that the feature attribute informationincludes, for example, information regarding the form of a feature suchas the feature type, shape, size, and image-recognized characteristicamount of the feature, and identification information such as thefeature ID for identifying the feature from other features. Informationconstituting such feature attribute information is generated based onthe image recognition result of the feature by the feature imagerecognizing unit 18, or the like.

Next, details of processing performed by the recognized featureinformation generating unit 43 will be explained using FIGS. 10A to 10Cand FIG. 11. FIGS. 10A to 10C are explanatory diagrams illustrating anoverview of learning processing of feature information F based on animage recognition result of a feature. FIG. 10A is an example of a roadmarking (feature) provided on the actual road on which the vehicle C istraveling. In this example, in a construction section according toexisting construction information W stored in the construction databaseDB3, a feature f1 of characters “30” representing the maximum speedlimit is provided. The feature image recognizing unit 18 executes theimage recognition processing for feature learning in this constructionsection taking the speed marking “30” as a target type. FIG. 10B is anexample of recognized feature information A stored in the learningdatabase DB4 when feature learning processing is performed under theroad condition shown in FIG. 10A. FIG. 10C is an example of the featuredatabase DB2 on which learned result stored in the learning database DB4is reflected. In addition, the example shown in FIG. 10 shows a casewhere only one feature exists in the construction section according tothe existing construction information W. However, there may be a casewhere a plurality of features exist. In this case, recognized featureinformation A is generated for each feature and learned.

In this embodiment, the recognition position information obtaining unit44 generates, as shown in FIG. 10B, recognition position information sfor each feature as a learning value for a predetermined position rangeto which the recognition position of the feature indicated by therecognition position information s belongs. Then, every time thisfeature is recognized, the recognized feature information generatingunit 43 adds up and stores the learning value as the recognitionposition information s in each of the position ranges, in a state ofbeing associated with feature attribute information obtained by thefeature attribute information obtaining unit 45. In this example, thepredetermined position range is a range sectioned and set for eachconstant distance in a direction along a link k included in roadinformation R, and is a range sectioned by, for example, every 0.5 [m]in the direction along the link k. Further, the learning value is avalue which is added, every time image recognition of one featuresucceeds, for a position range to which the recognition position of thefeature in the learning database DB4 belongs. For example, one point isadded every time image recognition of a feature succeeds. That is, inthis example, the recognition position information s is information of alearning value in each of the position ranges.

FIG. 11 is an enlarged diagram of a part of the learning value regardingthe feature f1 stored in the learning database DB4 shown in FIG. 10B.For example, in the example of FIG. 10A, when image recognition of thefeature f1 has succeeded and the recognition position of the feature f1obtained by the recognition position information obtaining unit 44 is aposition range shown as “a4” in FIG. 11, a value 1 is added to thelearning value of this position range a4 as shown by a dashed line inthis FIG. 11. Then, when the same feature f1 is image-recognized for aplurality of times due to the vehicle C passing the same road aplurality of times, learning values as a plurality of recognitionposition information s generated every time this feature is recognizedare added and accumulated in the learning database DB4 for everyposition range representing the recognition position of this feature, asshown in FIG. 10B and FIG. 11. Then, as will be explained later, whenthe learning value becomes a predetermined learning threshold T orlarger, learned feature information Fb for this feature is generated bythe learned feature information generating unit 47 and stored in thefeature database DB2. In the example of FIGS. 10A to 10C, the learnedfeature information Fb1 corresponding to the feature f1 is stored in thefeature database DB2 as shown in FIG. 10C.

Further, to make a feature indicated by recognition position informations identifiable from other features, the recognized feature informationgenerating unit 43 stores in the learning database DB4 feature attributeinformation of this feature obtained by the feature attributeinformation obtaining unit 45 in a state of being associated with therecognition position information s. Specifically, the recognized featureinformation A stored in the learning database DB4 includes informationof learning values of the respective position ranges as recognitionposition information s, and feature attribute information associatedtherewith. As described above, this feature attribute informationincludes, for example, information regarding the form of a feature suchas the feature type, shape, size, and image-recognized characteristicamount of this feature, and identification information such as thefeature ID for identifying this feature from other features.

17. Estimated Position Determining Unit

The estimated position determining unit 46 functions as an estimatedposition determining unit which determines, based on a plurality ofrecognition position information s for a same feature that is stored inthe learning database DB4 by recognizing the image of the same featurefor a plurality of times, an estimated position pg of this feature. Inthis embodiment, based on the plurality of recognition positioninformation s for the same feature stored in the learning database DB4,the estimated position determining unit 46 determines an estimatedrecognition position pa for this feature as shown in FIGS. 10B and 10C,and converts this estimated recognition position pa into the position ofthis feature on a road to thereby determine the estimated position pg ofthis feature. Specifically, based on a distribution of learning valuesas the plurality of recognition position information s for the samefeature, the estimated position determining unit 46 first determines arepresentative value of this distribution as the estimated recognitionposition pa for this feature. Here, a mode value is used as therepresentative value of the distribution. More specifically, theestimated position determining unit 46 determines that a position whichrepresents a position range, in which the learning value as recognitionposition information s for each feature becomes equal to or larger thanthe predetermined learning threshold T for the first time, is theestimated recognition position pa for this feature. As an example, adetermination method in the case of determining the estimatedrecognition position pa of the feature f1 in the example of FIGS. 10A to10C will be explained. As shown in FIG. 11, the learning value as therecognized feature information A for the feature f1 is equal to orlarger than the learning threshold T for the first time in the positionrange a4. Therefore, the estimated position determining unit 46determines the position representing the position range a4, for example,a center position pa4 of the position range a4, as the estimatedrecognition position pa of the feature f1.

Next, the estimated position determining unit 46 converts the estimatedrecognition position pa of the feature determined as described aboveinto the position of this feature on the road so as to determine theestimated position pg of this feature. Such conversion can be performedbased on a positional relationship between the vehicle C and a featurein image information G, which is theoretically obtained from anattaching position, an attaching angle, an angle of view, and the likeof the back camera 11 serving as an imaging device. Then, theinformation representing the estimated position pg of the featureobtained in this manner by the estimated position determining unit 46 isobtained as estimated position information of this feature.

18. Learned Feature Information Generating Unit

The learned feature information generating unit 47 functions as alearned feature information generating unit which associates estimatedposition information representing an estimated position of each featuredetermined by the estimated position determining unit 46 with featureattribute information representing the attribute of this feature tothereby generate learned feature information Fb. Here, the learnedfeature information Fb includes feature attribute information includedin recognized feature information A and estimated position informationrepresenting an estimated position pg of this feature obtained bystatistically processing a plurality of recognition position informations by the estimated position determining unit 46. Specifically, thelearned feature information generating unit 47 associates estimatedposition information representing an estimated position pg of eachfeature obtained by the estimated position determining unit 46 withfeature attribute information included in recognized feature informationA for this feature to generate learned feature information Fb.Accordingly, the learned feature information Fb is generated asinformation including position information and attribute information ofa feature, similarly to the initial feature information Fa. Then, thislearned feature information Fb generated by the learned featureinformation generating unit 47 is stored in the feature database DB2. Inthis embodiment, as shown in FIG. 10C, learned feature information Fb1is stored in the feature database DB2 in a state of being associatedwith information of a link k and node n according to road information Rstored in the map database DB1. Note that a dark square symbol “▪” shownin this diagram represents the estimated position pg of the feature f1indicated by the position information of the learned feature informationFb1.

Further, when it generates the learned feature information Fb in aconstruction section according to construction information W whosecompletion is determined by the construction completion determining unit36 and stores it in the feature database DB2, the learned featureinformation generating unit 47 performs processing to invalidate ordelete the initial feature information Fa having position information ofthis construction section. Accordingly, thereafter the learned featureinformation Fb is used instead of the initial feature information Fa forcorrection of vehicle position information P by the vehicle positioncorrecting unit 19. In this embodiment, initial feature information Fa1of the speed marking of “30” having position information in aconstruction section according to existing construction information W asshown in FIG. 10C is stored in the feature database DB2. Accordingly,the learned feature information generating unit 47 performs processingto invalidate this initial feature information Fa1 at the time ofstoring the learned feature information Fb1. In addition, in the casewhere learned feature information Fb having position information in aconstruction section according to existing construction information Whas already been stored in the feature database DB2 as in a case whereroad construction is performed in the same section of the road aplurality of times, processing to invalidate or delete this learnedfeature information Fb is also performed similarly.

19. Procedure of Vehicle Position Correction/Feature Learning Processing

Next, a procedure of vehicle position correction/feature learningprocessing according to this embodiment executed in the navigationdevice 1 including the vehicle position recognizing device 2 and thefeature information collecting device 3 will be explained. FIG. 12 is aflowchart showing the entire procedure of vehicle positioncorrection/feature learning processing according to this embodiment.Further, FIG. 13 is a flowchart showing a procedure of vehicle positioncorrection processing according to this embodiment, and FIG. 14 is aflowchart showing a procedure of feature learning processing accordingto this embodiment. Processing procedures which will be explained beloware executed by hardware or software (program) or both of themconstituting the above-described functional units. When theabove-described functional units are constituted by a program, thearithmetic processing device included in the navigation device 1operates as a computer that executes a vehicle position recognitionprogram or a feature information collecting program constituting theabove-described functional units. Hereinafter, explanation will be givenaccording to the flowcharts.

As shown in FIG. 12, in the vehicle position correction/feature learningprocessing in the navigation device 1, first, vehicle positioninformation P is obtained by the vehicle position information obtainingunit 14 (step #01). Then, based on the obtained vehicle positioninformation P, it is determined whether or not the vehicle C is in aconstruction section according to existing construction information Wstored in the construction database DB3 (step #02). This determinationis performed referring to all construction information W stored in theconstruction database DB3 shown in FIG. 9, and it is determined that,when the position of the vehicle C indicated by the vehicle positioninformation P obtained in step #01 is included in a construction sectionaccording to any one of construction information W stored in theconstruction database DB3, the vehicle C is in the construction section.When the vehicle C is not in a construction section according toexisting construction information W (step #02: No), then it isdetermined whether or not construction information W is obtained by theconstruction information obtaining unit 31 (step #03). As describedabove, the construction information W is information includinginformation of at least a construction section, and is received by theexternal information receiving unit 32 or generated by the constructioninformation generating unit 34 based on an image recognition result of aconstruction symbol wt (refer to FIG. 7) by the construction imagerecognizing unit 33. Then, in a state that no construction information Wis obtained by the construction information obtaining unit 31 (step #03:No), vehicle position correction processing by the vehicle positioncorrecting unit 19 is executed (step #04). This vehicle positioncorrection processing will be explained later in detail based on theflowchart shown in FIG. 13.

On the other hand, when construction information W is obtained by theconstruction information obtaining unit 31 (step #03: Yes), aconstruction section is set as a section to stop the vehicle positioncorrection processing, based on information of a construction sectionincluded in this construction information W (step #05). Further, thevehicle position information P is obtained by the vehicle positioninformation obtaining unit 14 (step #06). Then, based on the obtainedvehicle position information P, it is determined whether or not thevehicle C is in the construction section set in step #05 (step #07). Inthis determination, it is determined that the vehicle C is in theconstruction section when the position of the vehicle C indicated by thevehicle position information P obtained in step #06 is included in theconstruction section set in step #05. When the vehicle C is not in theconstruction section (step #07: No), the vehicle position correctionprocessing by the vehicle position correcting unit 19 is executed (step#04). Then, when the vehicle C is in the construction section (step #07:Yes), the vehicle position correction processing is stopped by thecorrection stop processing unit 35 (step #08). Thereafter, theprocessing returns to step #06, and the vehicle position correctionprocessing is stopped until the vehicle C exits from the constructionsection set in step #05 (step #08).

Further, based on the vehicle position information P obtained in step#01, when the vehicle C is in the construction section according to theexisting construction information W stored in the construction databaseDB3 (step #02: Yes), then construction completion determination isperformed by the construction completion determining unit 36 todetermine completion of the construction indicated by this existingconstruction information W (step #09). Then, when it is determined thatthe existing construction information W is completed (step #10: Yes),feature learning processing by the feature learning unit 41 is executed(step #11). This feature learning processing will be explained later indetail based on the flowchart shown in FIG. 14. On the other hand, whenit is determined that this existing construction information W is notcompleted (step #10: No), the processing proceeds to step #06 and thevehicle position correction processing is stopped until the vehicle Cexits from the construction section according to this existingconstruction information W (step #08). Thus, the entire procedure of thevehicle position correction/feature learning processing is finished.

Next, the procedure of vehicle position correction processing will beexplained. As shown in FIG. 13, in the vehicle position correctionprocessing, the navigation device 1 first obtains vehicle positioninformation P by the vehicle position information obtaining unit 14(step #21). Next, it obtains image information G by the imageinformation obtaining unit 13 (step #22). In this embodiment, thefeature image recognizing unit 18 performs image recognition processingof a feature, using image information G obtained by the back camera 11as a target. Therefore, image information G captured by the back camera11 is obtained here. Then, the feature image recognizing unit 18 obtainsfeature information F of a target feature ft from the feature databaseDB2 based on the vehicle position information P obtained in step #21(step #23). In this embodiment, based on the vehicle positioninformation P and the position information of a feature included in thefeature information F, feature information F of one feature existing inthe traveling direction of the vehicle C is obtained from the featuredatabase DB2, and the feature according to this feature information F isset as the target feature ft.

Thereafter, the feature image recognizing unit 18 executes the imagerecognition processing for position correction on the image informationG obtained in step #22 (step #24). When image recognition of the targetfeature ft has not succeeded in this image recognition processing forposition correction (step #25: No), the processing is finished as it is.On the other hand, when image recognition of the target feature ft hassucceeded in the image recognition processing for position correction(step #25: Yes), the vehicle position correcting unit 19 calculates apositional relationship between the vehicle C and the target feature ftbased on the image recognition result of this target feature ft (step#26). Then, the vehicle position correcting unit 19 corrects the vehicleposition information P based on the calculation result of step #26 andthe position information of the target feature ft included in thefeature information F obtained from the feature database DB2 (step #27).Thus, the procedure of the vehicle position correction processing isfinished.

Next, the procedure of feature learning processing will be explained. Asshown in FIG. 14, in the feature learning processing, the navigationdevice 1 first determines a target type by the target type determiningunit 42 (step #41). As described above, the target type is a featuretype used as a target for the image recognition processing for featurelearning by the feature image recognizing unit 18. Next, the featureimage recognizing unit 18 executes the image recognition processing forfeature learning to recognize the image of a feature of the target typedetermined in step #41 (step #42). Then, when image recognition of afeature has not succeeded in the image recognition processing forfeature learning (step #43: No), the processing proceeds to step #47. Onthe other hand, when image recognition of a feature has succeeded in theimage recognition processing for feature learning (step #43: Yes),recognition position information s representing a recognition positionof this feature is generated and obtained by the recognition positioninformation obtaining unit 44 (step #44). Further, feature attributeinformation representing an attribute of this feature is generated andobtained by the feature attribute information obtaining unit 45 (step#45). Then, the recognized feature information generating unit 43associates the recognition position information s with the featureattribute information to generate recognized feature information A, andthis information is stored in the learning database DB4 (step #46).

Thereafter, it is determined whether or not the vehicle C is in aconstruction section according to existing construction information Waccording to the determination in step #02 (step #47). When the vehicleC is in the construction section (step #47: Yes), the processing returnsto step #42. Therefore, until the vehicle C exits from the constructionsection according to this existing construction information W, the imagerecognition processing for feature learning is continued by the featureimage recognizing unit 18, and when image recognition of a feature hassucceeded, recognized feature information A of this feature is generatedand stored in the learning database DB4. On the other hand, when thevehicle C exits from the construction section according to the existingconstruction information W (step #47: No), then it is determined whetheror not a learning value as recognition position information s of afeature stored in the learning database DB4 is equal to or larger than apredetermined learning threshold T. When the learning value as therecognition position information s of a feature is smaller than thepredetermined learning threshold T (step #48: No), the processing isfinished as it is.

On the other hand, when the learning value as the recognition positioninformation s of the feature stored in the learning database DB4 isequal to or larger than the predetermined learning threshold T (step#48: Yes), an estimated position pg of this feature is determined by theestimated position determining unit 46 (step #49). Thereafter, thelearned feature information generating unit 47 associates the estimatedposition information representing the estimated position pg determinedin step #49 for this feature with feature attribute information includedin recognized feature information A for this feature to thereby generatelearned feature information Fb (step #50). Then, the generated learnedfeature information Fb is stored in the feature database DB2 (step #51).Thus, the procedure of the feature learning processing is finished.

20. Other Embodiments

(1) In the above-described embodiment, there is explained an example inwhich the correction stop processing unit 35 stops processing of thevehicle position correcting unit 19 while the position of the vehicle Cindicated by the vehicle position information P is in a constructionsection. However, the embodiment of the present invention is not limitedto this, and it is sufficient as long as processing of the vehicleposition correcting unit 19 can be stopped at least while the positionof the vehicle C is in a construction section. Therefore, it is alsopossible that the correction stop processing unit 35 is arranged to stopprocessing of the vehicle position correcting unit 19 within apredetermined distance before and after a construction section accordingto construction information W. With this arrangement, even wheninformation accuracy is low in the construction section of theconstruction information W obtained by the construction informationobtaining unit 31, it is possible to reliably stop processing of thevehicle position correcting unit 19 in the construction section.

(2) In the above-described embodiment, there is explained an example inwhich the learning threshold T is constant, for a generation conditionfor the learned feature information Fb by the learned featureinformation generating unit 47, specifically, the learning value as therecognition position information s. However, the embodiment of thepresent invention is not limited to this. In another preferredembodiment of the present invention, the feature learning unit 41 isarranged to compare an image recognition result of a feature by thefeature image recognizing unit 18 with feature information F havingposition information in a construction section stored in the featuredatabase DB2, and change the generation condition of the learned featureinformation Fb according to the degree of approximation therebetween.More specifically, when road construction is performed, it is possiblethat a feature existed in the construction section is changed, but it isalso possible that the feature is not changed. Further, there may alsobe a case that, even when the feature is changed, only the position ismoved and the feature type or the form thereof is not changed.Accordingly, considering such possibilities, it is preferable to have anarrangement in which, when the image of a feature that approximates to afeature existing before construction is recognized, learning isperformed easily and learned feature information Fb is quickly generatedand stored in the feature database DB2. More specifically, in anotherpreferred embodiment of the present invention, for example, the learningthreshold T is provided as a variable value, and the learning thresholdT is set low when an image recognition result of a feature and featureinformation F having position information in a construction sectionalmost match, or when only the position is different and the featuretype and form match.

(3) As described above, when road construction is performed, it ispossible that a feature existed in the construction section is changed,but it is also possible that the feature is not changed. Further, theremay also be a case that, even when the feature is changed, only theposition is moved and the feature type or the form thereof is notchanged. Accordingly, another preferred embodiment of the presentinvention is arranged such that, when the content of learned featureinformation Fb as a result of learning of a feature by the featurelearning unit 41 approximates to the initial feature information Fa, theinitial feature information Fa1 is adopted and the learned featureinformation Fb is invalidated. With such an arrangement, it becomespossible to adopt initial feature information Fa which is prepared inadvance as information having higher accuracy when a feature is notchanged by road construction.

(4) In the above-described embodiment, there is explained an example inwhich recognition position of a feature according to recognitionposition information s obtained by the recognition position informationobtaining unit 44 is the vehicle position information P when imagerecognition has succeeded. However, the recognition position of afeature according to recognition position information s is not limitedto this. Therefore, in another preferred embodiment of the presentinvention, for example, for a target feature for which image recognitionhas succeeded, based on vehicle position information P and an imagerecognition result of image information G, the position of this featureon a road with reference to the vehicle position information P iscalculated, and the position of this feature on the road is taken as therecognition position of the feature according to recognition positioninformation s.

(5) In the above-described embodiment, there is explained an example inwhich the estimated position determining unit 46 is arranged todetermine, based on a distribution of a plurality of recognitionposition information s for the same feature, the mode value of thisdistribution as the estimated recognition position pa of this targetfeature, and convert the estimated recognition position pa into theposition of the feature on the road so as to determine an estimatedposition pg of the feature. However, the determination method of theestimated position pg by the estimated position determining unit 46 isnot limited to this. Therefore, another preferred embodiment of thepresent invention is arranged such that, for example, based on thedistribution of recognition position information s, anotherrepresentative value such as an average or median of this distributionis determined as the estimated recognition position pa of this feature.

(6) In the above-described embodiment, there is explained an example inwhich the vehicle C is provided with both the front camera 12 and theback camera 11 as imaging devices, but the embodiment of the presentinvention is not limited to this. Another preferred embodiment of thepresent invention is arranged such that only one of the front camera 12and the back camera 11 is provided. In this case, the navigation device1 including the vehicle position recognizing device 2 and the featureinformation collecting device 3 according to the present inventionperforms generation of construction information W, correction of vehicleposition information P, and feature learning based on an imagerecognition result of image information G obtained by the one imagingdevice. Further, as an imaging device other than the front camera 12 andthe back camera 11, for example, a side camera capturing a side of thevehicle can also be used.

(7) In the above-described embodiment, there is explained an example inwhich the entire structure of the navigation device 1 is mounted in thevehicle C. However, the embodiment of the present invention is notlimited to this. Specifically, another preferred embodiment of thepresent invention is arranged such that part or all of the components,except those required to be mounted in the vehicle C, such as theimaging devices (the back camera 11 and the front camera 12), thevehicle position information obtaining unit 14 and the like are providedin a server device that is provided outside of the vehicle C and isconnected communicably to the vehicle C via a wireless communicationline or the like.

(8) In the above-described embodiment, there is explained an example inwhich the feature information collecting device 3 according to thepresent invention is applied to the navigation device 1. However, theembodiment of the present invention is not limited to this. Therefore,it is also possible of course to apply the present invention to anotherstructure different from that of the above-described embodiment. Forexample, the feature information collecting device 3 according to thepresent invention may be used for a map database creating device or thelike.

The present invention can be preferably used for a feature informationcollecting device and a feature information collecting program thatrecognizes the image of a feature included in image information obtainedfrom an imaging device or the like mounted in a vehicle and collectsinformation of the feature, as well as for a vehicle positionrecognizing device and a navigation device using them.

1. A feature information collecting device, comprising: a vehicleposition information obtaining unit that obtains vehicle positioninformation representing a current position of a vehicle; an imageinformation obtaining unit that obtains image information in asurrounding area of the vehicle; a feature image recognizing unit thatperforms image recognition processing of a feature included in the imageinformation; a construction information obtaining unit that obtainsconstruction information including information of a constructionsection; a construction information storage unit that stores theconstruction information obtained by the construction informationobtaining unit; a construction completion determining unit thatdetermines, when the vehicle travels a section of a road correspondingto a construction section according to the construction informationalready stored in the construction information storage unit, completionof construction indicated by the construction information; a featurelearning unit that causes the feature image recognizing unit to performimage recognition processing of a feature in a construction sectionaccording to the construction information when the completion ofconstruction is determined by the construction completion determiningunit, and that generates, based on an image recognition result thereofand the vehicle position information, learned feature informationincluding position information and attribute information of animage-recognized feature; and a feature database that stores initialfeature information including position information and attributeinformation which are prepared in advance for a plurality of features,wherein the feature learning unit causes the feature image recognizingunit to perform image recognition in the construction section givingpriority to a feature of a same type as a feature according to theinitial feature information having position information in theconstruction section.
 2. The feature information collecting deviceaccording to claim 1, wherein the construction information obtainingunit includes a construction information receiving unit that receivesthe construction information from a transmitter disposed outside of thevehicle.
 3. The feature information collecting device according to claim1, wherein the construction information obtaining unit includes aconstruction image recognizing unit that performs image recognitionprocessing of a construction symbol included in the image informationobtained by the image information obtaining unit, and a constructioninformation generating unit that generates the construction informationbased on an image recognition result of a construction symbol by theconstruction image recognizing unit.
 4. The feature informationcollecting device according to claim 3, wherein the construction imagerecognizing unit performs image recognition processing of at least oneof a construction notice sign, a construction fence, a constructionbarricade, a security light, a cone, and a construction guide humanmodel as the construction symbol.
 5. The feature information collectingdevice according to claim 3, wherein the construction informationgenerating unit sets a predetermined section with reference to arecognition position of the construction symbol as information of theconstruction section included in the construction information.
 6. Thefeature information collecting device according to claim 5, wherein whenconstruction symbols are included in image information of a plurality ofconsecutive frames, the construction information generating unit sets astart point of the construction section with reference to a recognitionposition of a first construction symbol included in image information ofa front side of the vehicle, and sets an end point of the constructionsection with reference to a recognition position of a last constructionsymbol included in image information of a rear side of the vehicle. 7.The feature information collecting device according to claim 3, whereinwhen a construction notice sign is image-recognized as the constructionsymbol by the construction image recognizing unit and a constructionsection is recognized based on an image recognition result of theconstruction notice sign, the construction information generating unitsets information of a construction section included in the constructioninformation according to a recognition result of the constructionsection.
 8. The feature information collecting device according to claim1, wherein when the vehicle travels a section of a road corresponding toa construction section according to the construction information alreadystored in the construction information storage unit, the constructioncompletion determining unit determines that construction indicated bythe construction information is completed when construction informationincluding a same construction section is not obtained by theconstruction information obtaining unit.
 9. The feature informationcollecting device according to claim 1, wherein when the constructioninformation includes information of a construction period, theconstruction completion determining unit determines that constructionindicated by the construction information is completed if theconstruction period according to the construction information is overwhen the vehicle travels a section of a road corresponding to aconstruction section according to the construction information alreadystored in the construction information storage unit.
 10. The featureinformation collecting device according to claim 1, wherein when thevehicle does not travel a section of a road corresponding to aconstruction section according to the construction information alreadystored in the construction information storage unit for a predeterminedtime period, the construction information is deleted from theconstruction information storage unit.
 11. The feature informationcollecting device according to claim 1, wherein the feature learningunit comprises: a recognition result storage unit that storesrecognition position information, which represents a recognitionposition of a feature by the feature image recognition unit and isobtained based on the vehicle position information, and attributeinformation of the feature in an associated manner; an estimatedposition determining unit that determines, based on a plurality of therecognition position information for a same feature, which are stored inthe recognition result storage unit due to the same feature beingimage-recognized a plurality of times, an estimated position of thefeature; and a learned feature information generating unit thatgenerates learned feature information by associating positioninformation representing an estimated position of each featuredetermined by the estimated position determining unit with attributeinformation of the feature.
 12. The feature information collectingdevice according to claim 1, wherein the feature learning unit comparesan image recognition result of a feature by the feature imagerecognizing unit with the initial feature information having positioninformation in the construction section and changes a generatingcondition for the learned feature information according to a degree ofapproximation therebetween.
 13. The feature information collectingdevice according to claim 1, further comprising: a database that storesthe learned feature information.
 14. A vehicle position recognizingdevice, comprising: a feature information collecting device according toclaim 1; and a vehicle position correcting unit that checks an imagerecognition result of a feature by the feature image recognizing unitwith the learned feature information for the feature and corrects thevehicle position information.
 15. A navigation device, comprising: avehicle position recognizing device according to claim 14; a mapdatabase in which map information is stored; an application program thatoperates referring to the map information; and a guidance informationoutput unit that operates according to the application program andoutputs guidance information.
 16. A non-transitory computer-readablestorage medium storing a feature information collecting program, theprogram comprising: instructions for obtaining vehicle positioninformation representing a current position of a vehicle; instructionsfor obtaining image information in a surrounding area of the vehicle;instruction for performing image recognition processing of a featureincluded in the image information; instruction for obtainingconstruction information including information of a constructionsection; instruction for storing the obtained construction informationin a construction information storage unit; instruction for determining,when travels a section of a road corresponding to a construction sectionaccording to the construction information already stored in theconstruction information storage unit, completion of constructionindicated by the construction information; instruction for performingrecognition processing of a feature in a construction section accordingto the construction information when the completion of construction isdetermined, and generating, based on an image recognition result thereofand the vehicle position information, learned feature informationincluding position information and attribute information of animage-recognized feature; instructions for accessing stored initialfeature information including position information and attributeinformation which are prepared in advance for a plurality of features;and instructions for performing image recognition in the constructionsection giving priority to a feature of a same type as a featureaccording to the initial feature information having position informationin the construction section.